Vehicle dynamics control systems are increasingly used in automotive vehicles to improve vehicle safety and satisfy government regulations. Examples of such systems include vehicle active safety systems like vehicle electronic stability control (ESC), comprehensive safety vehicle (CSV), and vehicle lane change assist. For those safety systems to operate effectively, accurate and timely knowledge of vehicle dynamic states are required. One important vehicle dynamic state is the vehicle lateral velocity, which reflects the lateral dynamics of a motor vehicle. For example, in electronic stability control (ESC), vehicle lateral velocity is used to calculate the so-called “vehicle side-slip” angle, which is an important attribute of vehicle lateral dynamics in addition to the yaw rate signal. With side-slip control, the overall vehicle stability control can be more effective in both understeer and oversteer situations. It is also easy to understand that lateral velocity will be crucial for systems like lane change assist, which virtually controls the lateral direction of vehicle dynamics.
Currently there is no production sensor for vehicle velocities that is cost effective. As such, this signal is generally obtained through estimation methods using commonly available sensors such as wheel speed sensors, accelerometers, yaw rate sensors, and other related sensors. There are mainly two categories of methods of estimating vehicle lateral velocity: methods-based observer or Kalman filter theories with a simplified model of the vehicle lateral dynamics, and methods-based tire force estimation together with road surface identification. While many variations of the above two basic methods are proposed to deal with specific difficulties, the main hurdles of the above methods still remain. For the observer/Kalman filter type of methods, all the techniques proposed depends on a model of the vehicle lateral dynamics, such as the so-called Bicycle model or Kinematics model. As no model can effectively reflect all the operating regions of the vehicle dynamics, the methods in this category cannot accurately determine lateral velocity outside the fidelity of the used model. For the second category of methods based on tire force/road surface, the main drawback is that the accuracy of the lateral velocity estimation depends heavily on the accuracy of both tire force and road surface estimations, and on efficiency of the used routine for numerical integration; all those factors are problematic themselves.
Accordingly, it is desirable to implement a system and method for accurately estimating a vehicular velocity in all dynamic regions of a vehicle's operations. In addition, it is desirable to implement such a system and method using the commonly available sensors previously described. Furthermore, other desirable features and characteristics of the present invention will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and the foregoing technical field and background.